Quantitative Microbial Risk Assessment Model in R

We have developed an open-source implementation of the FDA-iRISK comparative risk assessment framework using the R programming language. This model performs quantitative microbial risk assessment (QMRA) for foodborne hazards by simulating pathogen behavior from processing through consumption, ultimately estimating public health burden in disability-adjusted life years (DALYs).

Model Structure
The model follows a modular, stage-based process:
Initial conditions: User-defined prevalence and concentration distributions for the pathogen at the starting point of the supply chain
Process stages: Sequential nodes representing steps such as partitioning, storage (growth or die-off), and handling, each with mathematically defined effects on pathogen prevalence and concentration
Exposure assessment: Monte Carlo simulation combining final pathogen concentration with serving size distributions to generate ingested dose distributions
Hazard characterization: Pathogen-specific dose-response models (Beta-Poisson for Salmonella, exponential for Listeria) that convert dose to probability of illness
Risk characterization: Multiplication of per-serving risk by annual eating occasions and DALY weights to estimate total annual burden

Implementation
The model is implemented in R using the mc2d package for multivariate Monte Carlo simulation, enabling separation of variability and uncertainty. All code is provided as an executable R Markdown file with embedded functions for:

Three validated case studies (Salmonella in peanut butter; Listeria in soft cheese and cantaloupe)
Intervention scenario testing (e.g., contamination reduction, temperature control)
Uncertainty analysis for dose-response parameters
Enhanced visualizations using ggplot2

Key Features
Transparency: Fully open-source code enables independent verification and peer review
Flexibility: Modular design simplifies adding new food-hazard pairs, modifying process stages, or incorporating new data
Reproducibility: Fixed random seed ensures identical results across runs
Advanced analytics: Built-in uncertainty analysis and customizable visualization functions

Validation
The model successfully replicated results from Chen et al. (2013), with outputs closely matching published estimates (e.g., 63.05 vs. 63.5 annual DALYs for Salmonella in peanut butter).

Applications
This tool supports evidence-based food safety decision-making by enabling users to compare risks across food-hazard pairs, evaluate intervention effectiveness, and communicate uncertainty in risk estimates. The model is freely available for researchers, regulators, and industry professionals.